Electrical stimulation of the nervous system, including the brain and spinal cord, exists as a potentially effective, but also risky treatment option for various medical conditions such as pain, epilepsy, dystonia, Parkinson's disease and depression. In addition to potential health complications arising from surgical implantation of stimulation hardware (e.g., an implanted pulse generator (IPG) and a stimulation electrode), there exists the problem of determining what type of stimulation each patient should receive. Since the brain anatomy and electrode placement is different between patients, there is no standard course of treatment that is universally applicable to all patients. For example, the same stimulation parameters (e.g., pulse width, pulse duration, amplitude, polarity, etc.) tend to produce different results for different patients.
Additionally, stimulation hardware is advancing so as to provide healthcare professionals with an ever-growing number of stimulation options. For example, IPGs are currently available which are capable of simultaneously producing outputs of different amplitude, e.g., for different electrodes and different leadwires. Electrode leads have also advanced to include multiple contacts that, when combined with the IPGs, allow individual contacts to be stimulated in different ways, whereas older-generation leads required the same stimulation signal to be applied to all contacts in a given lead. Accordingly, there is a need for tools that allow for accurate and efficient determinations of what combinations of stimulation parameters are required for a given patient.
One known approach to predicting stimulation effects involves the creation of brain stimulation field models (SFMs), also referred to as volumes of activation (VOAs) or volumes of tissue activated (VTAs). For example, U.S. Pat. No. 7,346,382 to McIntyre et al., the contents of which is incorporated herein by reference, describes the use of axon or neuron models which may be used to calculate an estimated VOA that results from a given stimulation parameter combination, and describes a target VOA to which such estimated VOAs may be compared.
Predicting stimulation effects by modeling estimated VOAs using axon or neuron models can involve a large processor load, and can require assumptions about the biophysics of the brain. For instance, it may require assumptions about the distribution density or the triggering thresholds of axons in a particular brain region.